Defect detection during an automated production process

ABSTRACT

Described herein are improvements for identifying defects during automated item production. In one example, a method includes identifying a first defect in a first item. The first defect is associated with a stage of production of the first produced item. The method further includes retrieving first parametric data associated with the stage for the first item and identifying one or more defect indicators based on the first parametric data and second parametric data associated with the stage for one or more second items having defects associated with the stage. The method also includes monitoring subsequent parametric data associated with the stage to recognize the one or more defect indicators in the subsequent parametric data.

RELATED APPLICATIONS

This application is related to and claims priority to U.S. ProvisionalPatent Application 62/760,663, titled “IMPROVED DEFECT DETECTION DURINGAN AUTOMATED PRODUCTION PROCESS,” filed Nov. 13, 2018, and which ishereby incorporated by reference in its entirety.

TECHNICAL BACKGROUND

Automated item production processes (e.g., manufacturing/assemblyprocesses) may include multiple stages that each perform a designatedoperation that result in a completed item upon completion of a finalstage. For example, one stage may place a component of an item and thena next stage may attach the component to the item in that place. At eachstage, it is possible that one or more defects may be introduced intothe item. In the above example, the component may not be placedcorrectly at the placement stage or the component may not be attachedcorrectly at the attachment stage. Typically, the earlier a defect isdetected, the easy and/or more cost-effective correcting that defectwill be. For instance, an incorrect placement of the component fromabove may affect the placement of additional components in later stages.If the initial defect was caught before those other components are alsoplaced incorrectly, then it will be easier to fix the one componentbefore allowing the item to continue to the subsequent stages.

Overview

Described herein are improvements for identifying defects duringautomated item production. In one example, a method includes identifyinga first defect in a first item. The first defect is associated with astage of production of the first produced item. The method furtherincludes retrieving first parametric data associated with the stage forthe first item and identifying one or more defect indicators based onthe first parametric data and second parametric data associated with thestage for one or more second items having defects associated with thestage. The method also includes monitoring subsequent parametric dataassociated with the stage to recognize the one or more defect indicatorsin the subsequent parametric data.

In some embodiments, the method includes identifying third parametricdata associated with one or more other stages of the production of thefirst item and identifying the one or more defect indicators furtherbased on the third parametric data. In those embodiments, the method mayinclude identifying fourth parametric data associated with the one ormore other stages for the one or more second items.

In some embodiments, upon recognizing the one or more defect indicatorsin the subsequent parametric data for a second item, the method includesremoving the second item from the production process.

In some embodiments, the method includes adjusting a configuration ofone or more stages of the production process based on the one or moredefect indicators.

In some embodiments, the method includes identifying fourth parametricdata associated with one or more third items not having defectsassociated with the stage and identifying the one or more defectindicators further based on the fourth parametric data.

In some embodiments, identifying the one or more defect indicatorscomprises determining one or more attributes that the first parametricdata shares with the second parametric data and using at least a firstportion of the one or more attributes as the one or more defectindicators. In those embodiments, the one or more attributes may includea pattern of values in the first parametric data. Also in thoseembodiments, using at least a portion of the one or more attributes asthe one or more defect indicators may comprise identifying fourthparametric data associated with one or more third items not havingdefects associated with the stage and including ones of the one or moreattributes that the first parametric data and the second parametric datado not also share with the fourth parametric data in the first portion.

In some embodiments, the first parametric data comprises one or moremeasurements determined for the first item in association with thestage.

In another embodiment, an apparatus is provided having one or morecomputer readable storage media and a processing system operativelycoupled with the one or more computer readable storage media. Programinstructions stored on the one or more computer readable storage media,when read and executed by the processing system, direct the processingsystem to identify a first defect in a first item, wherein the firstdefect is associated with a stage of production of the first item andretrieve first parametric data associated with the stage for the firstitem. The program instructions further direct the processing system toidentify one or more defect indicators based on the first parametricdata and second parametric data associated with the stage for one ormore second items having defects associated with the stage. Also, theprogram instructions direct the processing system to monitor subsequentparametric data associated with the stage to recognize the one or moredefect indicators in the subsequent parametric data.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example implementation for identifying defectsduring automated item production.

FIG. 2 illustrates an operational scenario for identifying defectsduring automated item production.

FIG. 3 illustrates an example implementation for identifying defectsduring automated item production.

FIG. 4 illustrates an operational scenario for identifying defectsduring automated item production.

FIG. 5 illustrates exemplary parametric data and defect indicators foridentifying defects during automated item production.

FIG. 6 illustrates an operational scenario for identifying defectsduring automated item production.

FIG. 7 illustrates an operational scenario for identifying defectsduring automated item production.

FIG. 8 illustrates an operational scenario for identifying defectsduring automated item production.

FIG. 9 illustrates an example computing architecture for identifyingdefects during automated item production.

DETAILED DESCRIPTION

When a stage of an automated item production process performs itsdesignated task, parametric data is collected about the performance ofthat task. The parametric data indicates one or more measured valuesthat can be captured by the automated production component(s) performingthe operations for the stage. These measured values are used to detect adefect caused by the stage of the process even if the values are withinthe tolerance thresholds of the stage's operation. That is, measuredvalues that fall outside of the tolerance thresholds may indicate that adefect may have occurred at the step and are easy to recognize. However,it is possible that a defect is created even when the measured values ofthe parametric data are within the tolerance thresholds. Since thetolerance thresholds would not be triggered in such a situation, thedefect may not be recognized until later on in the production process ormaybe even once the item is in use by a customer. The parametric data isused herein to identify defects despite the measured values fallingwithin tolerance thresholds. This allows a defect to be recognizedbefore the item is passed to the next stage in the production process(i.e., in substantially real-time during the process), which minimizesany correction needed to account for the defect.

FIG. 1 illustrates implementation 100 for identifying defects duringautomated item production. Implementation 100 includes automation defectsystem 101, automation stage 132 component(s) 102, and automation stage133 component(s) 103. Stage 132 and stage 133, performed by respectiveautomation stage 132 component(s) 102 and automation stage 133component(s) 103, are two of what could be many more stages of an itemproduction process. Those additional stages may come before, after,and/or between stage 132 and stage 133 and are performed by their ownrespective automation stage components. In this example, stage 132 isperformed before stage 133. Each of automation stage 132 component(s)102 and automation stage 133 component(s) 103 may include nozzles,heads, drive motors, arms, belts, control circuitry, or any other typeof component that could perform an operation in an item productionprocess—including combinations thereof. Automation defect system 101 andautomation stage 132 component(s) 102 communicate over communicationlink 111. Automation defect system 101 and automation stage 133component(s) 103 communicate over communication link 112. Communicationlinks 111-112 may be direct links or may include one or more interveningsystems, networks, and/or devices.

FIG. 2 illustrates operational scenario 200 for identifying defectsduring automated item production. In operational scenario 200,automation defect system 101 identifies a defect in an item beingproduced by the automated production process having stage 132 and stage133 (201). In this example, automation defect system 101 identifies thedefect by receiving defect information 121 from automation stage 133component(s) 103. In this example, defect information 121 includes anyinformation that automation defect system 101 may need to identify thatthe defect was caused at stage 132 and to identify the item in which thedefect was found. For example, the item may have a unique identifier totrack the item's progress through the automated production process,which may be included in defect information 121. Likewise, stage 132 maybe identified explicitly through an identifier for stage 132 or mayidentify a component (e.g., microchip) of the item (e.g., processingboard) that is causing the item to be defective and rely on automationdefect system 101 to determine that stage 132 is associated with thatcomponent. Stage 133 may identify the defect due to stage 133 involving,at least in part, an item testing/inspection operation to ensure theitem has been assembled properly up to that point (e.g., an opticalinspector that measures aspects of the item resulting from stage 133).The testing/inspection of stage 132's results may also occur in stage132 itself or in another testing/inspection step, which may not beconsidered a production step. In other examples, human testers,including end customers of the item, may be the ones to identify theitem defect to automation defect system 101 using information similar todefect information 121.

Upon receiving defect information 121, automation defect system 101retrieves parametric data 122, which is associated with stage 132 forthe item (202). As noted above, parametric data includes measured valuestaken from when an item was going through an operation of the processstage. In this case, parametric data 122 indicates one or more measuredvalues from when automation stage 132 component(s) 102 were operating onthe item to complete stage 132. For instance, if automation stage 132component(s) 102 apply solder paste to the item at stage 132, parametricdata 122 may indicate a volume of solder paste applied to variouslocations on the item, the area covered by the solder paste at eachlocation, the height of the solder paste from the surface at eachlocation, or other quantifiable value—including combinations thereof.Parametric data 122 may be received by automation defect system 101 fromautomation stage 132 component(s) 102 after receiving defect information121 or may be received at some other time, such as periodically or afterthe values for each item passing through stage 132 are measured. Assuch, retrieving parametric data 122 may comprise automation defectsystem 101 accessing a data storage component, either internal orexternal, that stores parametric data 122.

Automation defect system 101 processes parametric data 122, along withadditional parametric data 123 to identify one or more defect indicators124 (203). Parametric data 123 includes parametric data retrieved byautomation defect system 101 for other items that had defects identifiedto have been caused by stage 132. A machine learning algorithm (or othertype of similarly capable artificial intelligence) may be used byautomation defect system 101 to determine commonalities and/or patternsthat exist in the parametric data, including commonalities/patternsacross values, for items having defects caused at stage 132. Parametricdata 123 in some examples may also include parametric data for itemsthat did not include defects from stage 132, so automation defect system101 can use that parametric data as a control to make sure an identifiedcommonality/pattern is not also a characteristic of items withoutdefects caused at stage 132. In further examples, parametric data 123may include parametric data from other stages of the automatedproduction process for the defective items. In those examples,automation defect system 101 may identify commonalities/patterns acrossstages just in case a defect, which was seemingly caused at stage 132,was also affected by another stage. By identifyingcommonalities/patterns in parametric data, automation defect system 101is able to identify defect indicators 124 in measured values that fallwithin operational tolerances and would not otherwise indicate a defecton their own. For example, automation defect system 101 may determinethat value A is greater than X and value B is less than Y every time (orabove a threshold percentage of times) a defect in an item is identifiedto have occurred at stage 132. Value A being greater than X and value Bbeing less than Y would then be included in defect indicator(s) 124 toindicate that a defect is likely to have occurred at stage 132. Thus,even if value A and value B were still within tolerance thresholds, thecombination of A being greater than X and B being less than Y wouldstill indicate a defect.

Defect indicator(s) 124 are then used to monitor subsequent parametricdata associated with stage 132 to recognize defect indicator(s) 124 insubsequent parametric data measured for subsequent items. To monitor thesubsequent parametric data, automation defect system 101 may transferdefect indicator(s) 124 to automation stage 132 component(s) 102.Automation stage 132 component(s) 102 can then recognize whethermeasured values satisfy any of defect indicator(s) 124. For example, atesting/inspection component may be provided with defect indicator(s)124 to recognize defects using defect indicator(s) 124 duringtesting/inspection. If one of defect indicator(s) 124 is recognized inthe measured values for an item, then automation stage 132 component(s)102 can flag the item as defective before passing the item along to asubsequent stage. Over time, automation defect system 101 may continueto update defect indicator(s) 124 based on additional defective itemsthat are identified either through defect indicator(s) 124 or later on(e.g., at stage 133). Defect indicator(s) 124 therefore improve defectdetection by allowing defects to be identified based on values measuredduring operation at a production stage and without having to actuallyinspect or test an item for defects. Moreover, defect indicator(s) 124may be used as a basis for adjusting settings or components ofautomation stage 132 component(s) 102 (either automatically or with userinteraction, including replacing components) to prevent or reduce thenumber of defects caused at stage 132 in the future.

In some examples, automation defect system 101 may provide informationto user 141 about defect indicator(s) 124. This allows user 141 to beaware of issues in the automatic production process and potentially takecorrective action if automation stage 132 component(s) 102 are notconfigured to automatically correct for defect indicator(s) 124.Automation defect system 101 may include a user interface to directlypresent to user 141 (e.g., display or audibly present information 125)or automation defect system 101 may present information 125 to user 141via another system, such as a user device operated by user 141, to whichinformation 125 is transferred.

While the above example appears to show automation defect system 101implemented in a single device, it should be understood that automationdefect system 101 may be distributed across multiple devices and may beincluded in devices having other functionality, such as a control systemfor the automated production process. In some examples, automationdefect system 101 may be implemented at least partially using a cloudcomputing service.

FIG. 3 illustrates implementation 300 for identifying defects duringautomated item production. Implementation 300 includes automation defectsystem 301, automation stage components 302, automation stage components303, automation stage components 304, automation stage components 305,network 306, and user device 307. Automation stage components 302,automation stage components 303, automation stage components 304, andautomation stage components 305 when operating together performrespective stages 332, 333, 334, and 335 of automated process 331.Network 306 may comprise a single communication network, such as a localarea network (LAN), or may include multiple networks, such as multipleLANs and/or a wide area network (WAN) like the Internet, over whichcommunications are transferred between automation defect system 301,automation stage components 302, automation stage components 303,automation stage components 304, automation stage components 305, anduser device 307. In some examples, one or more of automation stagecomponents 302, automation stage components 303, automation stagecomponents 304 and automation stage components 305 may be connected to aprocess control system for automated process 331, which in turn connectsto network 306, rather than being directly connected to network 306, asshown. Communications over network 306 allow automation defect system301 to be a local system to automated process 331 or to be remotesystem, such as a cloud service system, for automated process 331 andpotentially other automated processes.

In operation, automated process 331 produces items in stages 332, 333,334, and 335, where each stage performs one or more tasks towardsproducing a final item. While not shown, automated process 331 mayinclude additional stages, which are performed by additional components,that are not shown in this example. Automation defect system 301 usesparametric data 322 received from automation stage components 302,automation stage components 303, automation stage components 304, andautomation stage components 305 during operation to learn attributesthat indicate an item is defective. Those attributes are used in defectindicators 323 by automation defect system 301 to identify otherdefective items in automated process 331. In this example, user 341interacts with automation defect system 301 via user device 307. Userdevice 307 may be a personal computer, tablet computer, smartphone, orsome other user operable device that communicates over network 306. Itshould be understood that, while the discussion below describes elementssuch as AI model 321 and stages 332-335 as performing tasks, it isactually the systems and devices associated therewith that areperforming the functions (e.g., AI model 321 executes on and directsautomation defect system 301 to perform as described below andautomation stage components 304 implement stage 334).

FIG. 4 illustrates operational scenario 400 for identifying defectsduring automated item production. Operational scenario 400 follows theproduction of item 401 through stage 332, stage 333, stage 334, andstage 335. Item 401 is first operated on by stage 332 at step 1. Uponcompletion of stage 332, parametric data 422 is transferred byautomation stage components 302 to automation defect system 301 at step2 for storage with parametric data 322 at step 3. Steps 1-3 then repeatthemselves three more times as item 401 is operated on at stage 333,then stage 334, and then stage 335. Upon completion of all stages, stage332 now includes parametric data 422, parametric data 423, parametricdata 424, and parametric data 425 for item 401. Parametric data 422further includes parametric data for other items, both defective andnon-defective, that went through stage 332, stage 333, stage 334, andstage 335.

In this example, user 341 operates user device 307 to provide defectinformation 421 to Artificial Intelligence (AI) model 321 executing onautomation defect system 301. Defect information 421 indicates to AImodel 321 that a defect occurred in item 401 that was caused by stage332, stage 333, stage 334, and/or stage 335 even though the measurementsin parametric data 422, parametric data 423, parametric data 424, andparametric data 425 were otherwise within acceptable tolerances. AImodel 321 is configured to process parametric data 322 at step 5 torecognize commonalities/patterns of attributes in parametric data fordefective items, which now includes parametric data 422-425, that arenot present in parametric data for non-defective items. Those attributesare then added to defect indicators 323 by AI model 321 so that futuredefective items can be identified using defect indicators 323. As moreparametric data is added to parametric data 322 for both defective andnon-defective items, AI model 321 will be able to further hone defectindicators 323 through machine learning thanks to having a larger samplesize. It should be understood that AI model 321 may be able to recognizecommonalities and patterns that are far more complex than thosedescribed in the specific examples herein.

In some examples, one or more of the attributes may be shared with oneor more non-defective items. For instance, AI model 321 may determinethat, of all the items that share a particular attribute, a highpercentage are defective (e.g., 97%). AI model 321 may use a threshold(e.g., greater than 95% defective) to determine that the attributeshould still be included in a defect indicator since there is only asmall chance that an item having the attribute will not be defective.The example attribute above would, therefore, be used for a defectindicator since 97% defective is greater than the 95% defectivethreshold.

FIG. 5 illustrates parametric data 522 and defect indicators 523 foridentifying defects during automated item production. Parametric data522 is an example of parametric data 322 and defect indicators 523 areexamples of defect indicators 323. Parametric data 522 includesmeasurements included in parametric data for items 401 and 501-505 fromeach of stages 332-335. For example, the measurements for item 401 ateach of stages 332-335 may have been included in parametric data 422,parametric data 423, parametric data 424, and parametric data 425,respectively, in operational scenario 400. Similarly, the measurementsfor items 501-505 may have been added to parametric data 522 upon eachof items 501-505 passing through stages 332-335 in a manner similar toitem 401 in operational scenario 400.

Each measurement shown in parametric data 522 has generic units butcould use any unit of measurement depending on what is performed by arespective stage. For example, the units of one stage may be time units(e.g., seconds) while another stage may be volume units (e.g.,milliliters) and another stage is length (e.g., millimeters). While onlyone measurement is included for each stage in parametric data 522, itshould be understood that parametric data 522 could include multiplemeasurements for one or more of the stages, with those multiplemeasurements possibly having different units. Since each stage mayperform multiple operations, or compound operations, on an item,multiple measurements may be appropriate to check that each operation,or portion of an operation, was performed properly.

Defect indicators 523 include defect indicator 511 and defect indicator512 that define measurements that AI model 321 has determined correspondto defective items. In this example, defect indicators 523 are definedusing boolean logic although other definition types may be used in otherexamples.

FIG. 6 illustrates operational scenario 600 for identifying defectsduring automated item production. Operational scenario 600 describes anexample for how AI model 321 may determine defect indicators 523 fromparametric data 522. AI model 321 identifies common attributes betweendefective items of parametric data 522 (601). In this example,parametric data 522 indicates that item 401, item 503, and item 504 aredefective due to something that occurred during stages 332-335. Thus, AImodel 321 determines which attributes are common between two or more ofitem 401, item 503, and item 504. An attribute for a defective item mayinclude a single measurement or may be a combination of measurements.Likewise, an attribute may include a description of one or more of themeasurements for an item. For instance, an attribute of item 401 may bethat item 401 has less than 4.1 units at stage 332 and greater than 7.5units at stage 335.

To help ensure that non-defective items do not also include similarattributes, AI model 321 modifies any attributes that also describenon-defective items in parametric data 522 (602). In this case, item501, item 502, and item 505 are not defective, so AI model 321references the measurements of those items. Using the attribute of item401 from above (i.e., less than 4.1 units at stage 332 and greater than7.5 units at stage 335), that attribute would be modified by AI model321 since item 505, which is not defective, also measured less that 4.1units at stage 332 and greater than 7.5 at stage 335. In that case, AImodel 321 may modify the attribute to indicate that item 401 has lessthan 4.0 units at stage 332. Since no non-defective items measured lessthan 4.0 at stage 332, then AI model 321 will use the attribute thatindicates less than 4.0 at stage 332 and greater than 7.5 units at stage335 for determining defect indicators 523.

Once AI model 321 has determined that the attributes AI model 321identified for defective items 401, 503, and 504 do not also describeany of the non-defective items, AI model 321 creates one or more defectindicators 523 using the attributes (603). AI model 321 may use eachdetermined attribute individually as a defect indicator or may combinetwo or more of the attributes into a single defect indicator, eithernewly created or combined into an existing defect indicator. AI model321 may include logic for determining which attributes should beincorporated into a defect indicator. The logic may include indicatorsof confidence that an attribute truly represents a defective item. Forinstance, the logic may include a threshold indicating a minimum numberof defective items to which an attribute applies before the attributecan be included in a defect indicator. The logic may also indicate to AImodel 321 which measurements are likely to affect other measurements.For example, it may be physically impossible for stage 332 to effect orbe affected by stage 335. Therefore, AI model 321 would not include anattribute concerning those two stages, such as the example attributediscussed above, in defect indicators 523 (although, such rules for AImodel 321 may be avoided to avoid incorrectly defining an impossiblesituation). Other decision logic may also be used or may be determinedby AI model 321 itself as it learns more from parametric data foradditional items going through automated process 331.

FIG. 7 illustrates operational scenario 700 for identifying defectsduring automated item production. Operational scenario 700 is an exampleof how defect indicators 523 may be used to recognize defective itemsgoing through automated process 331. As a subsequent item passes throughstages 332-335 of automated process 331, subsequent parametric datameasured for the subsequent item is passed to automation defect system301 in a manner similar to that of steps 1 and 2 of operational scenario400 (701). AI model 321 analyzes the subsequent parametric data todetermine whether the measurements carried therein satisfy any of defectindicators 523 (i.e., defect indicator 511 or defect indicator 512)(702). That is, AI model 321 determines whether the subsequentparametric data indicates 305 units at stage 334 and greater than 8.1units at stage 335 for the subsequent item, and also determines whetherthe subsequent parametric data indicates 56.2 units at stage 333 andless than 4.0 units at stage 332 for the subsequent item. If AI model321 determines that a defect indicator is satisfied, then AI model 321may be configured to stop comparing the subsequent parametric data tothose defect indicators that have not yet be checked, which reducesprocessing especially in situations where there are a large number ofdefect indicators to check. In other cases, AI model 321 may continue tocompare the subsequent parametric data to at least some of the remainingdefect indicators. In those cases, matches to more than one defectindicator may increase the confidence of AI model 321 that thesubsequent item is defective or may indicate increased severity of thedefect(s) in the subsequent item. Similarly, in some examples, differentweights may be given to different defect indicators. For example, AImodel 321 may be provided with information from user 341 about theseverity of a defect when being notified of a defective item (e.g., onedefect may be one that is easily fixable to salvage an item whileanother defect may result in having to scrap the defective item).

Upon determining that one or more of defect indicators 532 match thesubsequent parametric data, AI model 321 removes subsequent item fromautomated process 331 (703). AI model 321 may remove the subsequent itemfrom automated process 331 by directing a component further down inautomated process 331 to reject the subsequent item from the productionpath. In another example, AI model 321 may transfer a notification touser device 307, or another interested user's user device, to notifyuser 341 (or the other interested user) that the subsequent item isdefective. The user can then manually pull the subsequent item fromproduction either mid automated process 331 or at some other pointbefore the subsequent item reaches a customer (or other potentiallyeffected party), or may determine to handle the situation in some othermanner. In some cases, the user notification may indicate informationbeyond the fact that AI model 321 has determined the subsequent item tobe defective. For example, AI model 321 may indicate a severity of adefect (e.g., based on how many defect indicators matched the subsequentparametric data and/or the weight of the defect indicator(s) thatmatched), may indicate which stage(s) likely caused the defect (e.g.,matching defect indicator 511 may indicate to AI model 321 that stage334 and 335 caused the defect), may indicate specific measurements thatcaused the match to a defect indicator, or may provide some otherinformation that AI model 321 may learn from the parametric dataincluding combinations thereof. Additional information may help the userin their decision-making process about how to handle the subsequentitem.

AI model 321 may automatically add the subsequent parametric data toparametric data 522 to further refine defect indicators 523 or may waitfor confirmation from a user indicating that the subsequent item is, infact, defective before potentially including parametric data that couldincorrectly skew AI model 321's analysis. Even in examples wheresubsequent parametric data does not match any of defect indicators 523,AI model 321 may wait to include the subsequent parametric data thereinuntil either a confirmation is received indicating that the subsequentitem is not defective or a predetermined period of time has elapsedsince receiving the subsequent parametric data without AI model 321being notified that the subsequent data was defective.

In operational scenario 700, AI model 321 is charged with identifyingdefective items using defect indicators 523. In other examples, AI model321 may determine defect indicators 523 and then transfer them toanother component of automation defect system 301 or another monitoringsystem. For instance, automation defect system 301 may be a cloud basedsystem that uses AI model 321 to create defect indicators 522 forautomated process 331. Another system local to automated process 331,such as a process control system, may receive defect indicators 523 fromAI model 321 and then monitor items passing through automated process331 for defects indicated by defect indicators 523.

FIG. 8 illustrates operational scenario 800 for identifying defectsduring automated item production. In operational scenario 700, AI model321 attempts to salvage a subsequent item in automated process 331before the subsequent item becomes defective, as indicated by one ofdefect indicators 523. In this example, AI model 321 receives subsequentparametric data for the subsequent item at stage 332, stage 333, andstage 334 but not yet from stage 335 because the subsequent item has notreached or completed stage 335 (801). From the subsequent parametricdata, AI model 321 determines that 305 units was measured for thesubsequent item at stage 334 (802). Based on that measurement, AI model321 determines that defect indicator 511 could potentially be satisfiedif greater than 8.1 units is measured at stage 335 (803). Since thesubsequent item has yet to complete stage 335, AI model 321 transfersinstructions to stage 335 that adjust the configuration of stage 335 inan attempt to avoid a defect associated with defect indicator 511 forthe subsequent item (804). For example, the configuration of stage 335may be such that it accounts for tolerances that allow for measurementsup to 8.5 units, but that configuration may be adjusted on an item byitem basis. In this example, AI model 321 adjusts the configuration ofstage 335 for the subsequent item to account for tolerances that onlyallow measurements less than 8.1 units. While there will still be achance that stage 335 could operate sub-optimally and result in ameasurement of greater than 8.1 units, the modified configuration foroperating on the subsequent item will decrease the likelihood that themeasurement will exceed 8.1 units and cause satisfaction of defectindicator 511.

Once stage 335 is completed for the subsequent data item, subsequentparametric data from stage 335 for the subsequent data item istransferred to AI model 321. AI model 321 checks that subsequent dataitem to see whether it measures greater than 8.1 units and, therefore,satisfies defect indicator 511. If defect indicator 511, and any otherdefect indicator that may now be satisfied upon receipt of thesubsequent parametric data from stage 335, is not satisfied, then thesubsequent item is allowed to proceed in automated process 331.Otherwise, AI model 321 will determine that there is a defect in thesubsequent item despite re-configuring stage 335. AI model 321 may thenperform as described in operational scenario 700 to remove thesubsequent item form automated process 331.

In some examples, statistics may be maintained by automation defectsystem 301 about items determined to be defective based on defectindicators 523. The statistics may include information regarding howmany defects are caused by each stage or combination of stages,measurements corresponding to those defects, frequency of defects, orother statistical information that may be relevant to adjustingautomated process 331 to further reduce defective items—includingcombinations thereof. The statistics may be provided to user 341 viauser device 307, so user 341 can determine whether adjustments toautomated process 331 are needed, or automated defect system 301 mayadjust automated process 331 itself. For example, the statistics mayindicate that the situation concerning defect indicator 511, describedin operational scenario 800 above, occurs far more often than defectsrelated to defect indicator 512. Accordingly, automation defect system301, user 341, or some other entity with access to the statistics, maydetermine to adjust the configuration of automation stage components 305and/or automation stage components 304 permanently to better avoiddefects indicated by defect indicator 511.

FIG. 9 illustrates computing architecture 900 for identifying defectsduring automated item production. Computing architecture 900 is anexample computing architecture for automation defect system 101,although alternative configurations may also be used. Computingarchitecture 900 comprises communication interface 901, user interface902, and processing system 903. Processing system 903 is linked tocommunication interface 901 and user interface 902. Processing system903 includes processing circuitry 905 and memory device 906 that storesoperating software 907.

Communication interface 901 comprises components that communicate overcommunication links, such as network cards, ports, RF transceivers,processing circuitry and software, or some other communication devices.Communication interface 901 may be configured to communicate overmetallic, wireless, or optical links. Communication interface 901 may beconfigured to use TDM, IP, Ethernet, optical networking, wirelessprotocols, communication signaling, or some other communicationformat—including combinations thereof.

User interface 902 comprises components that interact with a user. Userinterface 902 may include a keyboard, display screen, mouse, touch pad,or some other user input/output apparatus. User interface 902 may beomitted in some examples.

Processing circuitry 905 comprises processing circuitry, such as amicroprocessor, and other circuitry that retrieves and executesoperating software 907 from memory device 906. Memory device 906comprises one or more computer readable storage media, such as a diskdrive, flash drive, data storage circuitry, or some other memoryapparatus that in no cases should be considered a mere propagatedsignal. Operating software 907 comprises computer programs, firmware, orsome other form of machine-readable processing instructions. Operatingsoftware 907 includes parametric data module 908 and defect indicatormodule 909. Operating software 907 may further include an operatingsystem, utilities, drivers, network interfaces, applications, or someother type of software. When executed by circuitry 905, operatingsoftware 907 directs processing system 903 to operate computingarchitecture 900 as described herein.

In particular, parametric data module 908 directs processing system 903to identify a first defect in a first item, wherein the first defect isassociated with a stage of production of the first produced item, andretrieve first parametric data associated with the stage for the firstitem. Defect indicator module 909 directs processing system 903 toprocess the first parametric data, along with additional parametric dataassociated with the stage for one or more other items having defectsassociated with the stage, to identify one or more defect indicators.Defect indicator module 909 further directs processing system 903 tomonitor subsequent parametric data associated with the stage torecognize the one or more defect indicators in the subsequent parametricdata.

The descriptions and figures included herein depict specificimplementations of the claimed invention(s). For the purpose of teachinginventive principles, some conventional aspects have been simplified oromitted. In addition, some variations from these implementations may beappreciated that fall within the scope of the invention. It may also beappreciated that the features described above can be combined in variousways to form multiple implementations. As a result, the invention is notlimited to the specific implementations described above, but only by theclaims and their equivalents.

What is claimed is:
 1. A method for improving defect detection in aproduction process, the method comprising: identifying a first defect ina first item, wherein the first defect is associated with a stage ofproduction of the first item; retrieving first parametric dataassociated with the stage for the first item, wherein the firstparametric data includes measured values taken during processing of thefirst item in the stage; providing the first parametric data and secondparametric data associated with the stage for one or more second itemshaving defects associated with the stage to a machine learningalgorithm; generating, as output from the machine learning algorithm,one or more defect indicators generated based on patterns identified inthe first parametric data and the second parametric data, wherein theone or more defect indicators identify defects created when the measuredvalues fall within operational tolerances; and monitoring subsequentparametric data associated with the stage to recognize the one or moredefect indicators in the subsequent parametric data for subsequentitems.
 2. The method of claim 1, further comprising: identifying thirdparametric data associated with one or more other stages of theproduction of the first item; and providing the third parametric data tothe machine learning algorithm, wherein the one or more defectindicators is generated based further on the third parametric data. 3.The method of claim 2, further comprising: identifying fourth parametricdata associated with the one or more other stages for the one or moresecond items.
 4. The method of claim 1, further comprising: uponrecognizing the one or more defect indicators in the subsequentparametric data for a third item, removing the third item from theproduction process.
 5. The method of claim 1, further comprising:adjusting a configuration of one or more stages of the productionprocess based on the one or more defect indicators.
 6. The method ofclaim 1, further comprising: identifying third parametric dataassociated with one or more third items not having defects associatedwith the stage; and providing the third parametric data to the machinelearning algorithm, wherein the one or more defect indicators isgenerated based further on the third parametric data not sharing thepatterns with the first parametric data and the second parametric data.7. The method of claim 1, further comprising: upon recognizing the oneor more defect indicators in the subsequent parametric data for a thirditem, flagging the item as defective before passing the item to a nextstage.
 8. The method of claim 1, further comprising: updating the one ormore defect indicators over time based on additional parametric dataassociated with the stage for additional defective items.
 9. The methodof claim 1, further comprising: displaying information regarding the oneor more defect indicators on a user interface.
 10. The method of claim1, further comprising: adjusting a component in the stage based on theone or more defect indicators to prevent or reduce the number of defectsat the stage.
 11. An apparatus for improving defect detection in aproduction process, the apparatus comprising: one or more computerreadable storage media; a processing system operatively coupled with theone or more computer readable storage media; and program instructionsstored on the one or more computer readable storage media that, whenread and executed by the processing system, direct the processing systemto: identify a first defect in a first item, wherein the first defect isassociated with a stage of production of the first item; retrieve firstparametric data associated with the stage for the first item, whereinthe first parametric data includes measured values taken duringprocessing of the first item in the stage; provide the first parametricdata and second parametric data associated with the stage for one ormore second items having defects associated with the stage to a machinelearning algorithm; generate, as output from the machine learningalgorithm, one or more defect indicators generated based on patternsidentified in the first parametric data and the second parametric data,wherein the one or more defect indicators identify defects created whenthe measured values fall within operational tolerances; and monitorsubsequent parametric data associated with the stage to recognize theone or more defect indicators in the subsequent parametric data forsubsequent items.
 12. The apparatus of claim 11, wherein the programinstructions further direct the processing system to: identify thirdparametric data associated with one or more other stages of theproduction of the first item; and provide the third parametric data tothe machine learning algorithm, wherein the one or more defectindicators is generated based further on the third parametric data. 13.The apparatus of claim 12, wherein the program instructions furtherdirect the processing system to: identify fourth parametric dataassociated with the one or more other stages for the one or more seconditems.
 14. The apparatus of claim 11, wherein the program instructionsfurther direct the processing system to: upon the one or more defectindicators being recognized in the subsequent parametric data for athird item, remove the third item from the production process.
 15. Theapparatus of claim 11, wherein the program instructions further directthe processing system to: adjust a configuration of one or more stagesof the production process based on the one or more defect indicators.16. The apparatus of claim 11, wherein the program instructions furtherdirect the processing system to: identify third parametric dataassociated with one or more third items not having defects associatedwith the stage; and provide the third parametric data to the machinelearning algorithm, wherein the one or more defect indicators isgenerated based further on the third parametric data not sharing thepatterns with the first parametric data and the second parametric data.17. The apparatus of claim 11, wherein the program instructions furtherdirect the processing system to: upon recognizing the one or more defectindicators in the subsequent parametric data for a third item, flag theitem as defective before passing the item to a next stage.
 18. Theapparatus of claim 11, wherein the program instructions further directthe processing system to: update the one or more defect indicators overtime based on additional parametric data associated with the stage foradditional defective items.
 19. The apparatus of claim 11, wherein, theprogram instructions further direct the processing system to: displayinformation regarding the one or more defect indicators on a userinterface.
 20. One or more non-transitory computer readable storagemedia having instructions stored thereon for improving defect detectionin a production process, the instructions, when read and executed by aprocessing system, direct the processing system to: identify a firstdefect in a first item, wherein the first defect is associated with astage of production of the first item; retrieve first parametric dataassociated with the stage for the first item, wherein the firstparametric data includes measured values taken during processing of thefirst item in the stage; provide the first parametric data and secondparametric data associated with the stage for one or more second itemshaving defects associated with the stage to a machine learningalgorithm; generate, as output from the machine learning algorithm, oneor more defect indicators generated based on patterns identified in thefirst parametric data and the second parametric data, wherein the one ormore defect indicators identify defects created when the measured valuesfall within operational tolerances; and monitor subsequent parametricdata associated with the stage to recognize the one or more defectindicators in the subsequent parametric data for subsequent items.